get_feed ¶

def get_feed(
    self,
    id: "capo_elementalinference.types.feed_id.FeedId",
    *,
    config_overrides: Optional[ElementalInferenceClientConfig] = None,
) -> "capo_elementalinference.types.get_feed_response.GetFeedResponse":
    """<p>Retrieves information about the specified feed.</p>

    Args:
        id: <p>The ID of the feed to query.</p>

    Raises:
        capo_elementalinference.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient access to perform this action.</p>
        capo_elementalinference.errors.internal_server_error_exception.InternalServerErrorException: <p>An internal server error occurred. This is a temporary condition and the request can be retried. If the problem persists, contact AWS Support. </p>
        capo_elementalinference.errors.resource_not_found_exception.ResourceNotFoundException: <p>The resource specified in the action doesn't exist.</p>
        capo_elementalinference.errors.too_many_request_exception.TooManyRequestException: <p>The request was denied due to request throttling. Too many requests have been made within a given time period. Reduce the frequency of requests and use exponential backoff when retrying. </p>
        capo_elementalinference.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_elementalinference.types.get_feed_request.GetFeedRequest]",
    ) -> OperationResponse[
        "capo_elementalinference.types.get_feed_response.GetFeedResponse"
    ]:
        import capo_elementalinference._operations.elemental_inference.get_feed

        output, http_response = (
            capo_elementalinference._operations.elemental_inference.get_feed.get_feed(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("GetFeed")
    )
    input_: capo_elementalinference.types.get_feed_request.GetFeedRequest = {
        "id": id
    }

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output